[Array Studio Video Tutorial] RNA-Seq Analysis Basic functions: Reads Quantification, Exon Junction and Gene Fusion Detection
RNA-Seq has become one of the most popular methods in gene and transcript level genomic research. It could help quantify gene and transcript expression, identify sequence variants and detect gene, transcript or exon level genomic events. Array Studio provides a variety of functions powerful enough for small and large scale genomic research. In this article, we will introduce a few basic and the most commonly used functions, including sequence quantification, gene annotation, exon junction detection and gene fusion detection.
- 1 Quantifying RNA-seq expression
- 2 Quantifying exon junction usage
- 3 Annotating Sequence Variants in your RNA-seq data
- 4 Gene Fusion detection in RNA-seq data
ArrayStudio provides a number of modules and options for RNA-Seq quantification at gene, transcript, exon and exon junction levels. Both FPKM and Count tables can be generated.
Alternative splicing has been shown to play an important role in a number of human diseases, including cancer, cardiovascular and neurodegenerative diseases. In Omicsoft Array Studio and the Land products, we provide modules and visualization functions that make it easier for users to research splicing. In RNA-Seq analysis, besides gene and transcript counts, Array Studio can report exon junction counts as well. Results can be visualized in Omicsoft's Genome Browser.
Mutation data allows user to compare mutation frequencies and research individual variants. Users can run the Summarize Variant Data module to annotate variants. Variants can be annotated in Mutation Reports or VCF files, and visualized directly in the Genome Browser.
Fusion genes can play an important role in cancer mutations that have multiple effects on a target gene. At Omicsoft, we provide a powerful fusion detection algorithm in FusionMap. FusionMap identifies unmapped reads that span multiple genomic locations, indicating possible gene fusion events:
Map Fusion Reads module will detect fusion genes from fusion junction-spanning reads which can characterize fusion genes at base pair resolution. This works with single end or paired end data. Combined Fusion Analysis will run fusion junction spanning + inter-transcript fusion read pairs detection at the same time. It detects fusion junction spanning reads from unmapped reads in BAM files, and detects inter-transcript fusion read pairs from singletons from BAM alignment entries. It will return a report showing potential fusion genes and counts for each fusion junction Combined fusion analysis can only be run on paired-end data.